CoreForge Atlas runs the full plan–write–test–ship loop on infrastructure we operate ourselves, instead of metering every task through someone else's frontier API.
Autonomous coding agents have crossed the line from demo to useful. The category leaders — Devin, Cursor Agent, Copilot Workspace, Cognition — all run on frontier APIs. A single agent task consumes an enormous number of tokens, because the loop retries, reads, tests, and revises before it produces anything a human sees.
That works while the compute is subsidised by venture funding. It does not survive contact with a real gross-margin conversation. The cost of a task is set by someone else's price list, and it is the largest line item in the business.
Open-weight models have closed most of the capability gap on coding work. Atlas runs a fine-tuned open-weight model on hardware we control, which converts a per-token variable cost into a fixed hourly one. Above a modest utilisation threshold, the economics invert.
We are explicit about what is ours and what is not. The base model is open-weight and Apache 2.0 licensed. The orchestration layer — the sandbox, the verification loop, the workforce system, repository context, integrations, and the Forge self-improvement engine — is the product, and it is where our engineering goes.
Effective cost per million tokens of agent work. Self-hosted cost is fixed hourly spend divided by realised throughput — it falls as utilisation rises, while API cost never moves.
The difference between generating code and shipping it is verification. Atlas runs each task in an isolated sandbox with the repository's real build and test commands, and does not surface work a human has to babysit.
Issue, PR, or natural-language brief. Repo context assembled from git index.
Task decomposed into file-level changes by the workforce orchestrator.
Edits applied in an isolated Docker sandbox with the real toolchain.
Project's own build, test, and lint commands run against the change.
Pull request opened for human review through the Approval Center.
Stated plainly, because it will come out in diligence anyway. This is a working product at coreforgeatlas.com — not a slide deck.
| Component | State | Note |
|---|---|---|
| Application shell | Live | TanStack Start on Cloudflare Workers, auth, RLS, dashboard |
| Workforce orchestrator | Live | Multi-agent hiring, task graph, approval center, audit log |
| Sandbox execution | Live | Docker-isolated per-task env; code editor, terminal, build console |
| Repository integration | Live | GitHub OAuth + PAT, repo indexing, PR flow |
| Inference model | Live | Open-weight 27B running in production; Atlas-350M/2B pretraining on Kaggle for owned stack |
| Verification loop | Live | Build, test, and lint runners wired end-to-end; hardening reliability at scale |
| Forge self-improvement | In progress | Autonomous suggester → patcher → self-review pipeline |
| Domain fine-tune | Next | Pipeline built (Kaggle + RunPod); collecting real trace data |
| Paid pilots | Next | Design-partner outreach begins with this round |
Owned inference stack. Every task we run generates a training trace. That trace fine-tunes the next model. Competitors renting frontier APIs cannot capture that data legally or economically.
Verification data. Real build/test outcomes across real repositories is the scarcest asset in agentic coding. Atlas produces it by design.
Workforce + Forge. The orchestration layer improves itself through the Forge loop — Atlas patches Atlas, under human approval. Each cycle raises the floor.
Comparables that have raised on the strength of the category alone:
Their architecture is a thin orchestration layer over rented frontier inference — which means their unit economics and ours diverge as volume grows, in our favour. We are not competing on capability first; we are competing on the cost per shipped pull request.
Creator CoreForge Inc. is raising a $5M seed on a SAFE at a $60M post-money cap. Target close Q1 2026. Minimum check $10K; lead allocation available.
Verification loop reliability, sandbox hardening, Forge self-improvement, first two hires (systems + ML infra).
GPU serving capacity (A100/H100 reserved), fine-tuning runs on collected trace data, benchmark evaluations.
Design partners, onboarding tooling, first paid pilots, developer relations.
Valuation moves with de-risking, not with time. This is the earliest and lowest-priced entry point. Each milestone below removes a specific risk and repricing follows.
Application, workforce, sandbox, GitHub integration all shipped. Verification loop end-to-end reliability is the gating risk.
Atlas takes a real repository issue and opens a passing pull request without human intervention on 10 consecutive tasks.
Public benchmark resolve rate, with cost per task disclosed alongside. Self-hosted cost advantage documented on identical workloads.
5 teams running Atlas against production repositories under paid pilots. $10K–$25K MRR floor. Seed round follows.
Sarmed — Founder & CEO, Creator CoreForge Inc. Solo technical founder. Built and shipped the current Atlas platform end-to-end: the workforce orchestrator, sandbox, GitHub integration, Forge self-improvement engine, and training pipeline. Prior work in software engineering and AI systems.
Investors at this stage are underwriting the founder above everything else. The first two hires funded by this round are an ML infrastructure engineer and a systems engineer for the sandbox and verification path.
Happy to walk through the architecture, the cost model, and the code as it stands. No deck-only meetings — you'll see the product running.
sarmed@creatorcoreforge.com · coreforgeatlas.com · Creator CoreForge Inc.